size.cor | R Documentation |
This function performs sample size computation for testing Pearson's product-moment correlation coefficient based on precision requirements (i.e., type-I-risk, type-II-risk and an effect size).
size.cor(rho, delta, alternative = c("two.sided", "less", "greater"),
alpha = 0.05, beta = 0.1, write = NULL, append = TRUE,
check = TRUE, output = TRUE)
rho |
a number indicating the correlation coefficient under the null hypothesis, |
delta |
a numeric value indicating the minimum difference to be detected, |
alternative |
a character string specifying the alternative hypothesis,
must be one of |
alpha |
type-I-risk, |
beta |
type-II-risk, |
write |
a character string naming a text file with file extension
|
append |
logical: if |
check |
logical: if |
output |
logical: if |
Returns an object of class misty.object
, which is a list with following
entries:
call |
function call |
type |
type of analysis |
data |
matrix or data frame specified in |
args |
specification of function arguments |
result |
list with the result, i.e., optimal sample size |
Takuya Yanagida takuya.yanagida@univie.ac.at,
Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology - Using R and SPSS. New York: John Wiley & Sons.
Rasch, D., Pilz, J., Verdooren, L. R., & Gebhardt, G. (2011). Optimal experimental design with R. Boca Raton: Chapman & Hall/CRC.
size.mean
, size.prop
#-------------------------------------------------------------------------------
# Example 1: Two-sided test
# H0: rho = 0.3, H1: rho != 0.3
# alpha = 0.05, beta = 0.2, delta = 0.2
size.cor(rho = 0.3, delta = 0.2, alpha = 0.05, beta = 0.2)
#-------------------------------------------------------------------------------
# Example 2: One-sided test
# H0: rho <= 0.3, H1: rho > 0.3
# alpha = 0.05, beta = 0.2, delta = 0.2
size.cor(rho = 0.3, delta = 0.2, alternative = "greater", alpha = 0.05, beta = 0.2)
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